{
  "data": {
    "a": {
      "slug": "laguna-s-2-1",
      "name": "Laguna S 2.1",
      "vendor": "Poolside",
      "vendorUrl": "https://poolside.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Laguna S 2.1 is Poolside's open-weight Mixture-of-Experts model for agentic coding, 118B total and 8B active parameters, with switchable reasoning and a 1,048,576-token context, published on Hugging Face under OpenMDW-1.1.",
      "url": "https://www.anchorterminal.com/tools/laguna-s-2-1",
      "markdownUrl": "https://www.anchorterminal.com/tools/laguna-s-2-1.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/laguna-s-2-1.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/laguna-s-2-1.json",
      "dns": "laguna-s-2-1.grade.anchorterminal.com",
      "repo": "https://huggingface.co/poolside/Laguna-S-2.1",
      "license": "OpenMDW-1.1",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-S-2.1"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-S-2.1-FP8"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-S-2.1-INT4"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-S-2.1-NVFP4"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-S-2.1-NVFP4-mlx"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-S-2.1-GGUF"
        },
        {
          "registry": "ollama",
          "name": "laguna-s-2.1"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for the main repository and every build, although the card's metadata carries an unused gate description pointing to Poolside's privacy policy (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under OpenMDW-1.1. Running it yourself costs the hardware, 235.1 GB for the BF16 files, 96.0 GB for Poolside's Q4_K_M GGUF and 66 GB for Ollama's MLX NVFP4 build. Poolside's API and Featherless AI host it; Poolside's OpenRouter endpoints expired on 31 October 2026 (checked 2026-11-03).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/poolside/Laguna-S-2.1",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "coding",
        "reasoning",
        "tool-calling",
        "long-context",
        "openmdw-1.1",
        "safetensors",
        "gguf",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-08-19",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 35,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 75,
          "risk": 55,
          "upkeep": 41
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file.",
        "bestFor": "Agents doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context.",
        "strengths": [
          "Released support in llama.cpp, Ollama, vLLM, SGLang, Transformers and mlx-lm",
          "Poolside's own FP8, INT4, NVFP4, MLX and GGUF builds, plus DFlash draft models",
          "1,048,576 tokens of context, trained at that length per Poolside's build cards",
          "Permissive OpenMDW-1.1 licence with no gate, account or use policy",
          "Poolside's own API and Featherless AI host it"
        ],
        "weaknesses": [
          "No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls",
          "The GGUF card's Q4_K_M entry no longer matches the file, which was replaced in place",
          "No safety results and only general cautions for agent use",
          "The SGLang and TensorRT-LLM commands run the repository's Python with trust_remote_code",
          "Known limitations in third-party harnesses and JSON arguments, and only off or max thinking"
        ],
        "agentNotes": [
          "Quickest start is `ollama run laguna-s-2.1`, 96 GB for the default Q4_K_M, or the 66 GB nvfp4 tag on Apple Silicon",
          "On our reference machine try `llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536`, from a llama.cpp release with the laguna architecture (b11772 has it). The 96 GB file leaves little room for context in 107.4 GB",
          "With vLLM or SGLang use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1`, and with TensorRT-LLM `--tool_parser poolside_v1 --reasoning_parser laguna`",
          "Thinking is on by default in the template, pass `chat_template_kwargs` with `enable_thinking` false to turn it off, and keep `reasoning_content` from earlier turns",
          "The GGUFs ship set to 262,144 tokens. For 1M with llama.cpp add `--ctx-size 1048576 --rope-scaling yarn --rope-scale 128 --yarn-orig-ctx 8192`, which Poolside says can cost quality",
          "Sampling per the generation config is temperature 1.0 and top_k 20, and Poolside suggests temperature 0.7 and top_p 0.95 at 1M",
          "Expect occasional tool-schema slips in harnesses unlike Poolside's own, and validate JSON arguments, which Poolside lists as known limitations"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 75,
          "risk": 55,
          "upkeep": 41
        },
        "provenanceScore": 69,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run laguna-s-2.1\n# or, with vLLM on four GPUs:\nvllm serve poolside/Laguna-S-2.1 --tensor-parallel-size 4 --tool-call-parser poolside_v1 \\\n  --reasoning-parser poolside_v1 --enable-auto-tool-choice --default-chat-template-kwargs '{\"enable_thinking\": true}'\n# or, with llama.cpp and Unsloth's GGUF, a third party's conversion:\nllama-server -hf unsloth/Laguna-S-2.1-GGUF:UD-Q4_K_M --jinja",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"laguna-s-2.1\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/laguna-s-2-1"
      },
      "sameCompany": [
        "laguna-m-1",
        "laguna-xs-2-1"
      ],
      "weights": {
        "trainedBy": "Poolside",
        "params": "117.6B total, 8B active",
        "context": 1048576,
        "goodFor": "Long-horizon agentic coding and terminal work with up to 1M tokens of context, on a 128 GB machine at 4-bit or a multi-GPU server.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Laguna-S-2.1-NVFP4-mlx",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1-NVFP4-mlx",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 71.9,
            "memoryGB": 96,
            "format": "mlx",
            "runsOn": [
              "MLX"
            ]
          },
          {
            "name": "ollama laguna-s-2.1:nvfp4",
            "url": "https://ollama.com/library/laguna-s-2.1:nvfp4",
            "who": "ollama",
            "precision": "nvfp4",
            "sizeGB": 66,
            "memoryGB": 96,
            "format": "mlx",
            "runsOn": [
              "Ollama"
            ],
            "note": "Ollama's MLX build, for Apple Silicon. Size as Ollama shows it, rounded to whole GB, with a 1M context window"
          },
          {
            "name": "unsloth Laguna-S-2.1-GGUF (UD-Q4_K_M)",
            "url": "https://huggingface.co/unsloth/Laguna-S-2.1-GGUF",
            "who": "unsloth",
            "precision": "q4_k_m",
            "sizeGB": 73.12,
            "memoryGB": 96,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From Unsloth, a third party's conversion and not Poolside's. 73.12 GB of UD-Q4_K_M weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it"
          },
          {
            "name": "Laguna-S-2.1-INT4",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1-INT4",
            "who": "publisher",
            "precision": "int4 (w4a16)",
            "sizeGB": 99.7,
            "memoryGB": 128,
            "format": "safetensors"
          },
          {
            "name": "Laguna-S-2.1-NVFP4",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 99.7,
            "memoryGB": 128,
            "format": "safetensors",
            "note": "For NVIDIA Blackwell GPUs and DGX Spark"
          },
          {
            "name": "Laguna-S-2.1-GGUF Q4_K_M",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 96,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "laguna-s-2.1-Q4_K_M.gguf, 96,031,829,760 bytes, replaced on 27 July 2026 with a mixed-precision build that keeps sensitive layers in BF16. The build card still lists it as 68 GB. The repository also holds Q8_0 (128.8 GB, 192 GB of memory), F16 (235.2 GB) and a 2.2 GB DFlash draft"
          },
          {
            "name": "ollama laguna-s-2.1:latest",
            "url": "https://ollama.com/library/laguna-s-2.1:latest",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 96,
            "memoryGB": 128,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Size as Ollama's tags page shows it, rounded to whole GB. Its model blob (948255a146c0) isn't Poolside's GGUF file. The library also has q8_0 (128 GB), f16 (235 GB) and MLX tags"
          },
          {
            "name": "Laguna-S-2.1-FP8",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 131.3,
            "memoryGB": 192,
            "format": "safetensors"
          },
          {
            "name": "Laguna-S-2.1",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 235.1,
            "memoryGB": 512,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ]
          }
        ],
        "run": "llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536 --port 8000",
        "runBuild": "Laguna-S-2.1-GGUF Q4_K_M",
        "checked": "2026-10-21",
        "minMemoryGB": 96
      },
      "family": {
        "lab": "Poolside",
        "family": "Laguna 2 (XS.2, XS 2.1, S 2.1)",
        "url": "https://www.anchorterminal.com/open-weights/#poolside",
        "others": [
          {
            "name": "Laguna-XS-2.1",
            "url": "https://huggingface.co/poolside/Laguna-XS-2.1",
            "params": "33.44B (Hub safetensors count; the name gives only the size class XS)",
            "listing": "https://www.anchorterminal.com/tools/laguna-xs-2-1"
          },
          {
            "name": "Laguna-S-2.1-base",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1-base",
            "params": "not stated (no safetensors count on the Hub; the instruct model is 117.56B)"
          },
          {
            "name": "Laguna-XS.2",
            "url": "https://huggingface.co/poolside/Laguna-XS.2",
            "params": "33.44B (Hub safetensors count; the name gives only the size class XS)"
          }
        ],
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Poolside, Inc. (named in Poolside's Terms of Use)",
        "domain": "poolside.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://poolside.ai/legal/eula",
        "privacy": "https://poolside.ai/legal/privacy",
        "statusPage": "",
        "changelog": "https://docs.poolside.ai/release-notes/models",
        "securityTxt": "valid",
        "checked": "2026-10-21",
        "notes": [
          "The Hugging Face organisation poolside is verified, and Poolside's release post links the repository.",
          "Nothing is hosted for this listing, so the OpenMDW-1.1 licence stands in for terms of service and no privacy policy is scored.",
          "Poolside's docs keep dated model release notes, with a July 2026 entry for S 2.1.",
          "security.txt is PGP-signed, names a security contact at poolside.ai and expires on 1 April 2028. The domain's registration date wasn't checked."
        ],
        "score": 69
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/laguna-s-2-1.json",
      "live": {
        "slug": "laguna-s-2-1",
        "securityTxt": {
          "url": "https://poolside.ai/.well-known/security.txt",
          "state": "valid",
          "expires": "2028-04-01T12:00:00.000Z",
          "checkedAt": "2026-11-10T16:09:25.614312659Z"
        },
        "domain": {
          "domain": "poolside.ai",
          "registered": "2020-07-07",
          "source": "https://rdap.identitydigital.services/rdap/domain/poolside.ai",
          "checkedAt": "2026-11-08T14:58:52.089559761Z"
        },
        "updatedAt": "2026-11-10T16:09:25.614312659Z"
      }
    },
    "answer": "Laguna S 2.1 and LFM3-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). LFM3-VL-2B leads on reach, agent interface and documentation and upkeep.",
    "b": {
      "slug": "lfm3-vl-2b",
      "name": "LFM3-VL-2B",
      "vendor": "Liquid AI",
      "vendorUrl": "https://www.liquid.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "LFM3-VL-2B is Liquid AI's open-weight 1.92B vision-language model for on-device use, taking text, images and video with thinking, tool calling and a 128K-token context, released on 27 October 2026 with Liquid's own GGUF, FP8, NVFP4, MLX and ONNX builds.",
      "url": "https://www.anchorterminal.com/tools/lfm3-vl-2b",
      "markdownUrl": "https://www.anchorterminal.com/tools/lfm3-vl-2b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lfm3-vl-2b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lfm3-vl-2b.json",
      "dns": "lfm3-vl-2b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/LiquidAI/LFM3-VL-2B",
      "license": "LFM Open License v1.0 (free commercial use below $10M annual revenue)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM3-VL-2B"
        },
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM3-VL-2B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM3-VL-2B-MLX-4bit"
        },
        {
          "registry": "ollama",
          "name": "lfm3-vl"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Liquid's GGUF, MLX and ONNX repositories are not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under the LFM Open License v1.0, commercial use included for organisations with under $10M in annual revenue; above that, commercial use isn't licensed. Running it yourself costs little hardware, 1.10 GB for Liquid's Q4_K_M GGUF with its 0.20 GB projector file and 3.84 GB in BF16. Liquid's API also serves it, at prices shown in its console (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://docs.liquid.ai/lfm/models/lfm3-vl-2b",
      "llmsTxt": "https://docs.liquid.ai/llms.txt",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "vision",
        "video",
        "small",
        "edge",
        "reasoning",
        "tool-calling",
        "custom-licence",
        "safetensors",
        "gguf",
        "mlx",
        "onnx",
        "self-hosted"
      ],
      "lastRelease": "2026-11-09",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 58,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 90,
          "reach": 85,
          "risk": 40,
          "upkeep": 77
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A 1.92B vision model that reads images and video on an 8 GB machine, with a documented tool-call format and thinking. It runs today in llama.cpp, Ollama and LM Studio, while vLLM and SGLang support is still unreleased. Transformers needs repository code, and there are no safety results, security contact or data sources.",
        "bestFor": "Agents on a laptop, phone or robot that need to read screens, documents, images and video from a model under 2 GB at 4 bits",
        "strengths": [
          "Reads images and video on an 8 GB machine from Liquid's own GGUF",
          "Tool calling, deferred tools and thinking in a 1.92B vision model",
          "A 128K-token context",
          "Liquid answered most questions on the repository",
          "Liquid's own API serves it"
        ],
        "weaknesses": [
          "vLLM and SGLang support is unreleased, which the card says itself",
          "Loading in Transformers runs code from the repository",
          "No safety results and no security contact",
          "How to ask for JSON isn't documented for this model",
          "The licence stops free commercial use at $10M in annual revenue",
          "Not recommended for coding, multi-turn conversations or complex reasoning"
        ],
        "agentNotes": [
          "Start it with `llama-server -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-vl`, with llama.cpp b11960 or later",
          "Use temperature 0.1, top_k 50 and repetition penalty 1.1, per the card",
          "Video is sampled at 2 frames a second up to 768 frames, and each frame costs context. Video input in llama.cpp needs ffmpeg",
          "Don't plan on vLLM or SGLang until a release carries LFM3-VL",
          "Validate JSON output yourself",
          "Liquid doesn't recommend it for coding, multi-turn conversations or complex reasoning"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 90,
          "reach": 85,
          "risk": 40,
          "upkeep": 77
        },
        "provenanceScore": 73,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "llama-server -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --jinja\n# or\nollama run lfm3-vl",
        "http": "curl http://localhost:8080/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/lfm3-vl-2b"
      },
      "sameCompany": [
        "liquid-d1",
        "lfm2-5-2-6b",
        "lfm3-1-7b",
        "lfm3-audio-2b",
        "lfm3-omni-2b"
      ],
      "weights": {
        "trainedBy": "Liquid AI",
        "builtOn": "LiquidAI/LFM3-1.7B-Base",
        "params": "1.92B",
        "context": 128000,
        "goodFor": "Small on-device vision model for questions about video, documents, charts and app screens, finding objects and spatial reasoning, not for coding, multi-turn conversations or complex reasoning.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "LM Studio"
        ],
        "builds": [
          {
            "name": "LFM3-VL-2B",
            "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 3.84,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "Transformers"
            ],
            "note": "Loads in Transformers with the repository's own code. Liquid also publishes FP8 (2.59 GB) and NVFP4 (2.21 GB) safetensors builds and an ONNX repository"
          },
          {
            "name": "LFM3-VL-2B-GGUF Q8_0",
            "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B-GGUF",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 2.05,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp",
              "LM Studio"
            ],
            "note": "LFM3-VL-2B-Q8_0.gguf (1.85 GB) plus mmproj-LFM3-VL-2B-Q8_0.gguf (0.20 GB)"
          },
          {
            "name": "LFM3-VL-2B-GGUF Q4_K_M",
            "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 1.3,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp",
              "LM Studio"
            ],
            "note": "LFM3-VL-2B-Q4_K_M.gguf (1.10 GB) plus Liquid's projector file mmproj-LFM3-VL-2B-Q8_0.gguf (0.20 GB), from the Hub's file list. The repository also holds Q4_0, Q8_0, F16 and BF16 files"
          },
          {
            "name": "LFM3-VL-2B-MLX-4bit",
            "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B-MLX-4bit",
            "who": "publisher",
            "precision": "4bit",
            "sizeGB": 1.59,
            "memoryGB": 8,
            "format": "mlx",
            "runsOn": [
              "MLX-VLM"
            ],
            "note": "Liquid also publishes 5, 6 and 8-bit and BF16 MLX builds"
          },
          {
            "name": "ollama lfm3-vl",
            "url": "https://ollama.com/library/lfm3-vl",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 1.5,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Ollama's library build, size as its tags page shows it on 10 November 2026. The library also has q8_0, bf16, mxfp8 and nvfp4 tags"
          }
        ],
        "run": "llama-server -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --jinja",
        "runBuild": "LFM3-VL-2B-GGUF Q4_K_M",
        "checked": "2026-11-10",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Liquid AI",
        "family": "LFM3",
        "url": "https://www.anchorterminal.com/open-weights/#liquid",
        "others": [
          {
            "name": "LFM3-1.7B-Base",
            "url": "https://huggingface.co/LiquidAI/LFM3-1.7B-Base",
            "params": "1.73B"
          },
          {
            "name": "LFM3-1.7B",
            "url": "https://huggingface.co/LiquidAI/LFM3-1.7B",
            "params": "1.73B",
            "listing": "https://www.anchorterminal.com/tools/lfm3-1-7b"
          },
          {
            "name": "LFM3-Omni-2B",
            "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B",
            "params": "2.17B",
            "listing": "https://www.anchorterminal.com/tools/lfm3-omni-2b"
          },
          {
            "name": "LFM3-Audio-2B",
            "url": "https://huggingface.co/LiquidAI/LFM3-Audio-2B",
            "params": "2.00B",
            "listing": "https://www.anchorterminal.com/tools/lfm3-audio-2b"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Liquid AI, Inc. (the licensor named in the LFM Open License)",
        "domain": "liquid.ai",
        "domainRegistered": "2017-12-16",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/LiquidAI/LFM3-VL-2B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The licence names Liquid AI, Inc. as licensor. The domain's registration date is as recorded on the liquid-d1 listing on 8 October 2026",
          "www.liquid.ai/.well-known/security.txt answered 404 on 10 November 2026"
        ],
        "score": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/lfm3-vl-2b.json"
    },
    "edges": [
      {
        "by": 36,
        "category": "upkeep",
        "leads": "lfm3-vl-2b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 11 of 20, intended use, architecture, parameters and context are on the card, limitations, languages and a cut-off aren't. Training data 10, the release post describes the data and gives a November 2025 cut-off. Last change 15, 25 August 2026, 70 days back. Responsiveness 0, Poolside answered none of the 34 discussions. Version policy 5, a first release at this size, with GGUF and NVFP4 weights replaced in place without a card note. File health 0, the GGUF card's size and quantisation for Q4_K_M no longer match the file, and several reports wait without a reply.",
          "b": "Model card 17, Intended use (questions about video, documents, charts and app screens, finding objects, spatial reasoning), a limitation (not recommended for coding, multi-turn conversations or complex reasoning), architecture (language model, 14-layer vision encoder and projector), parameters (1.92B), context (128K) and 16 languages. Training data 5, A general description: the base's 22T tokens of pre-training with the kinds of data added since LFM2, then vision mid-training on image and video data, supervised fine-tuning, vision reinforcement learning and distillation. Last change 20, the last commit, on 9 November 2026, added the OpenVINO checkpoint to the card's inference table, a day before this check. Responsiveness 20, Of the 5 discussions that aren't pull requests (27 to 31 October 2026), 3 have a reply from a LiquidAI member, which is most. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 10, Config, template and card agree on the lfm3_vl architecture, the 128,000-token context and the vision settings in processor_config.json. 17 + 5 + 20 + 20 + 5 + 10 = 77."
        },
        "scores": {
          "a": 41,
          "b": 77
        }
      },
      {
        "by": 15,
        "category": "risk",
        "leads": "laguna-s-2-1",
        "name": "Risk",
        "reasons": {
          "a": "File safety 15 of 25, safetensors and GGUF only, but the repository ships model code behind auto_map and the card's SGLang and TensorRT-LLM commands use trust_remote_code. Provenance 15, the verified poolside organisation, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 5, general cautions only. Licence risk 15, OpenMDW-1.1 has no use policy, threshold or revocation clause. Security programme 5, a signed security.txt and a security contact on the card, with no public advisories found.",
          "b": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40."
        },
        "scores": {
          "a": 55,
          "b": 40
        }
      },
      {
        "by": 10,
        "category": "reach",
        "leads": "lfm3-vl-2b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 10 of 30, the smallest 4-bit build is Ollama's 66 GB MLX nvfp4, which needs 96 GB with a fifth added and fits our 128 GB reference machine. Runtimes 25, released llama.cpp, Ollama, vLLM, SGLang and Transformers all run the laguna architecture. Builds 15, Poolside publishes FP8, INT4, NVFP4, MLX and GGUF builds. Terms 10, OpenMDW-1.1 is permissive but not OSI-approved. Download 10, nothing is gated. Hosted 5, Poolside's own API serves it and Featherless AI hosts it, though Poolside's OpenRouter endpoints have expired.",
          "b": "Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.20 GB projector file, and 1.30 GB × 1.2 = 1.6 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture and the LFM3-VL projector (src/models/lfm3.cpp and tools/mtmd at b12242), Ollama's library has an lfm3-vl entry, and LM Studio's catalogue lists liquid/lfm3-vl-2b. Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-vl-2b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85."
        },
        "scores": {
          "a": 75,
          "b": 85
        }
      },
      {
        "by": 5,
        "category": "interface",
        "leads": "lfm3-vl-2b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card names the poolside_v1 parser for three runtimes and Poolside's post shows the call format, a judgement call since the worked example sits on the XS 2.1 card. Trained tool calling 20, several calls a turn. Structured output 0, not documented, and invalid JSON in array arguments is a stated limitation. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 1M tokens trained per Poolside's build cards.",
          "b": "Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, images and video, tools, deferred tools, tool results and the thinking switch. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through `tools=` in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card says its fine-tuning mix included tool calling and agentic traces. Structured output 5, the card's vLLM and SGLang commands set a structured-output backend and its fine-tuning table has a JSON extraction notebook, but nothing says how to ask this model for JSON. Liquid's API guide documents `response_format` with lfm3-1.7b as its example. Reasoning control 10, Liquid's chat-template page documents `enable_thinking=False` in apply_chat_template for LFM3, with reasoning on by default inside think tags, and the card documents `preserve_thinking` and reports a no-thinking configuration in its benchmarks. Context 10, 128K tokens of input and output per the card, and max_position_embeddings 128000 for the language model in config.json. 20 + 25 + 20 + 5 + 10 + 10 = 90."
        },
        "scores": {
          "a": 85,
          "b": 90
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Poolside",
        "b": "Liquid AI",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "OpenMDW-1.1",
        "b": "LFM Open License v1.0 (free commercial use below $10M annual revenue)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "yes",
        "name": "llms.txt"
      },
      {
        "a": "2026-08-19",
        "b": "2026-11-09",
        "name": "Last release"
      },
      {
        "a": "2026-07-21",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "2026-07-21",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "yes, with an opt-out",
        "b": "not read yet",
        "name": "Customer content may train models"
      },
      {
        "a": "not found in the text",
        "b": "not read yet",
        "name": "Terms restrict automated access"
      },
      {
        "a": "not found in the text",
        "b": "not read yet",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "yes",
        "b": "not read yet",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "yes",
        "b": "not read yet",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "2.3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Laguna S 2.1 and LFM3-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). LFM3-VL-2B leads on reach, agent interface and documentation and upkeep.",
        "question": "Which is better for AI agents, Laguna S 2.1 or LFM3-VL-2B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 55 against 40"
        ],
        "also": null,
        "goodFor": "Agents doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context.",
        "slug": "laguna-s-2-1",
        "watchFor": "No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls"
      },
      {
        "aheadOn": [
          "Reach, 85 against 75",
          "Agent interface, 90 against 85",
          "Documentation and upkeep, 77 against 41"
        ],
        "also": null,
        "goodFor": "Agents on a laptop, phone or robot that need to read screens, documents, images and video from a model under 2 GB at 4 bits",
        "slug": "lfm3-vl-2b",
        "watchFor": "vLLM and SGLang support is unreleased, which the card says itself"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-s-2-1.json",
        "title": "Agents-A1-4B vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-vl-2b.json",
        "title": "Agents-A1-4B vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1.json",
        "title": "Apertus v1.5 8B vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-vl-2b.json",
        "title": "Apertus v1.5 8B vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-s-2-1.json",
        "title": "Apodex-1.1-mini vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-vl-2b.json",
        "title": "Apodex-1.1-mini vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-s-2-1.json",
        "title": "Arete-27B vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-vl-2b.json",
        "title": "Arete-27B vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-s-2-1.json",
        "title": "BigBang-v1 vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-vl-2b.json",
        "title": "BigBang-v1 vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-laguna-s-2-1.json",
        "title": "Carbon-X vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-lfm3-vl-2b.json",
        "title": "Carbon-X vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-laguna-s-2-1.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-vl-2b"
      },
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-s-2-1"
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm3-vl-2b"
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-s-2-1"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-s-2-1.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-s-2-1"
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        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-lfm3-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-5-bolt-e4b-4gb.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-5-bolt-e4b-4gb"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "LFM3-VL-2B vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-nano-30b-a3b.json",
        "title": "LFM3-VL-2B vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-nano-4b.json",
        "title": "LFM3-VL-2B vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "LFM3-VL-2B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-super-120b-a12b.json",
        "title": "LFM3-VL-2B vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "LFM3-VL-2B vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-ultra-550b-a55b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-north-mini-code-1-0.json",
        "title": "LFM3-VL-2B vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-north-mini-code-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-occamy-1-0.json",
        "title": "LFM3-VL-2B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-olmo-3-7b-think.json",
        "title": "LFM3-VL-2B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ornith-1-5-122b.json",
        "title": "LFM3-VL-2B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ornith-1-5-35b-a3b.json",
        "title": "LFM3-VL-2B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ornith-1-5-397b.json",
        "title": "LFM3-VL-2B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ornith-1-5-9b.json",
        "title": "LFM3-VL-2B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-otel-2-0-llm-31b-it.json",
        "title": "LFM3-VL-2B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-otel-2-0-llm-31b-it"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen-agentworld-35b-a3b.json",
        "title": "LFM3-VL-2B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "LFM3-VL-2B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-4b-instruct-2507.json",
        "title": "LFM3-VL-2B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-0-8b.json",
        "title": "LFM3-VL-2B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-0-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-122b-a10b.json",
        "title": "LFM3-VL-2B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-2b.json",
        "title": "LFM3-VL-2B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-397b-a17b.json",
        "title": "LFM3-VL-2B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-4b.json",
        "title": "LFM3-VL-2B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-9b.json",
        "title": "LFM3-VL-2B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-6-27b.json",
        "title": "LFM3-VL-2B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-6-35b-a3b.json",
        "title": "LFM3-VL-2B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-8-27b.json",
        "title": "LFM3-VL-2B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-8-flash-next.json",
        "title": "LFM3-VL-2B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "LFM3-VL-2B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-coder-next.json",
        "title": "LFM3-VL-2B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "LFM3-VL-2B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-omni-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-2b-instruct.json",
        "title": "LFM3-VL-2B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "LFM3-VL-2B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-32b-instruct.json",
        "title": "LFM3-VL-2B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-4b-instruct.json",
        "title": "LFM3-VL-2B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-8b-instruct.json",
        "title": "LFM3-VL-2B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "LFM3-VL-2B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-recursive-flint.json",
        "title": "LFM3-VL-2B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ripple-65b-preview.json",
        "title": "LFM3-VL-2B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-sensenova-loop-9b-preview.json",
        "title": "LFM3-VL-2B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-smollm3-3b.json",
        "title": "LFM3-VL-2B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-spark-x2-6-4b.json",
        "title": "LFM3-VL-2B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-step-3-7-flash.json",
        "title": "LFM3-VL-2B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "LFM3-VL-2B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "LFM3-VL-2B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "LFM3-VL-2B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-unlimited-ocr-2.json",
        "title": "LFM3-VL-2B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-2b.json",
        "title": "LFM3-VL-2B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-4b.json",
        "title": "LFM3-VL-2B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-zaya1-8b.json",
        "title": "LFM3-VL-2B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 10,
        "edge": "lfm3-vl-2b",
        "key": "reach",
        "laguna-s-2-1": 75,
        "lfm3-vl-2b": 85,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 15,
        "edge": "laguna-s-2-1",
        "key": "risk",
        "laguna-s-2-1": 55,
        "lfm3-vl-2b": 40,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 5,
        "edge": "lfm3-vl-2b",
        "key": "interface",
        "laguna-s-2-1": 85,
        "lfm3-vl-2b": 90,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 36,
        "edge": "lfm3-vl-2b",
        "key": "upkeep",
        "laguna-s-2-1": 41,
        "lfm3-vl-2b": 77,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Laguna S 2.1 and LFM3-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). LFM3-VL-2B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "laguna-s-2-1": "A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file.",
      "lfm3-vl-2b": "A 1.92B vision model that reads images and video on an 8 GB machine, with a documented tool-call format and thinking. It runs today in llama.cpp, Ollama and LM Studio, while vLLM and SGLang support is still unreleased. Transformers needs repository code, and there are no safety results, security contact or data sources."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm3-vl-2b",
    "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm3-vl-2b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm3-vl-2b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm3-vl-2b.min.md"
  },
  "markdown": "Laguna S 2.1 and LFM3-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). LFM3-VL-2B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Laguna S 2.1: grade C, 61/100, rank #45 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-s-2-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-s-2-1.json\n- LFM3-VL-2B: grade C, 61/100, rank #72 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm3-vl-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm3-vl-2b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Laguna S 2.1 (C)\n\nGood for: Agents doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context.\n\nAhead on:\n- Risk, 55 against 40\n\nWatch for: No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls\n\n### LFM3-VL-2B (C)\n\nGood for: Agents on a laptop, phone or robot that need to read screens, documents, images and video from a model under 2 GB at 4 bits\n\nAhead on:\n- Reach, 85 against 75\n- Agent interface, 90 against 85\n- Documentation and upkeep, 77 against 41\n\nWatch for: vLLM and SGLang support is unreleased, which the card says itself\n\n\n## Why the edge\n\n### Documentation and upkeep: LFM3-VL-2B +36\n\n- Laguna S 2.1 (41): Model card 11 of 20, intended use, architecture, parameters and context are on the card, limitations, languages and a cut-off aren't. Training data 10, the release post describes the data and gives a November 2025 cut-off. Last change 15, 25 August 2026, 70 days back. Responsiveness 0, Poolside answered none of the 34 discussions. Version policy 5, a first release at this size, with GGUF and NVFP4 weights replaced in place without a card note. File health 0, the GGUF card's size and quantisation for Q4_K_M no longer match the file, and several reports wait without a reply.\n- LFM3-VL-2B (77): Model card 17, Intended use (questions about video, documents, charts and app screens, finding objects, spatial reasoning), a limitation (not recommended for coding, multi-turn conversations or complex reasoning), architecture (language model, 14-layer vision encoder and projector), parameters (1.92B), context (128K) and 16 languages. Training data 5, A general description: the base's 22T tokens of pre-training with the kinds of data added since LFM2, then vision mid-training on image and video data, supervised fine-tuning, vision reinforcement learning and distillation. Last change 20, the last commit, on 9 November 2026, added the OpenVINO checkpoint to the card's inference table, a day before this check. Responsiveness 20, Of the 5 discussions that aren't pull requests (27 to 31 October 2026), 3 have a reply from a LiquidAI member, which is most. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 10, Config, template and card agree on the lfm3_vl architecture, the 128,000-token context and the vision settings in processor_config.json. 17 + 5 + 20 + 20 + 5 + 10 = 77.\n\n### Risk: Laguna S 2.1 +15\n\n- Laguna S 2.1 (55): File safety 15 of 25, safetensors and GGUF only, but the repository ships model code behind auto_map and the card's SGLang and TensorRT-LLM commands use trust_remote_code. Provenance 15, the verified poolside organisation, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 5, general cautions only. Licence risk 15, OpenMDW-1.1 has no use policy, threshold or revocation clause. Security programme 5, a signed security.txt and a security contact on the card, with no public advisories found.\n- LFM3-VL-2B (40): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40.\n\n### Reach: LFM3-VL-2B +10\n\n- Laguna S 2.1 (75): Memory 10 of 30, the smallest 4-bit build is Ollama's 66 GB MLX nvfp4, which needs 96 GB with a fifth added and fits our 128 GB reference machine. Runtimes 25, released llama.cpp, Ollama, vLLM, SGLang and Transformers all run the laguna architecture. Builds 15, Poolside publishes FP8, INT4, NVFP4, MLX and GGUF builds. Terms 10, OpenMDW-1.1 is permissive but not OSI-approved. Download 10, nothing is gated. Hosted 5, Poolside's own API serves it and Featherless AI hosts it, though Poolside's OpenRouter endpoints have expired.\n- LFM3-VL-2B (85): Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.20 GB projector file, and 1.30 GB × 1.2 = 1.6 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture and the LFM3-VL projector (src/models/lfm3.cpp and tools/mtmd at b12242), Ollama's library has an lfm3-vl entry, and LM Studio's catalogue lists liquid/lfm3-vl-2b. Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-vl-2b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85.\n\n### Agent interface: LFM3-VL-2B +5\n\n- Laguna S 2.1 (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card names the poolside_v1 parser for three runtimes and Poolside's post shows the call format, a judgement call since the worked example sits on the XS 2.1 card. Trained tool calling 20, several calls a turn. Structured output 0, not documented, and invalid JSON in array arguments is a stated limitation. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 1M tokens trained per Poolside's build cards.\n- LFM3-VL-2B (90): Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, images and video, tools, deferred tools, tool results and the thinking switch. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through `tools=` in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card says its fine-tuning mix included tool calling and agentic traces. Structured output 5, the card's vLLM and SGLang commands set a structured-output backend and its fine-tuning table has a JSON extraction notebook, but nothing says how to ask this model for JSON. Liquid's API guide documents `response_format` with lfm3-1.7b as its example. Reasoning control 10, Liquid's chat-template page documents `enable_thinking=False` in apply_chat_template for LFM3, with reasoning on by default inside think tags, and the card documents `preserve_thinking` and reports a no-thinking configuration in its benchmarks. Context 10, 128K tokens of input and output per the card, and max_position_embeddings 128000 for the language model in config.json. 20 + 25 + 20 + 5 + 10 + 10 = 90.\n\n## Score by category\n\n| Category | Weight | Laguna S 2.1 | LFM3-VL-2B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 75 | 85 | LFM3-VL-2B +10 |\n| Risk | 20% (20 this run) | 55 | 40 | Laguna S 2.1 +15 |\n| Agent interface | 10% (10 this run) | 85 | 90 | LFM3-VL-2B +5 |\n| Documentation and upkeep | 10% (10 this run) | 41 | 77 | LFM3-VL-2B +36 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Laguna S 2.1 | LFM3-VL-2B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Poolside | Liquid AI |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | OpenMDW-1.1 | LFM Open License v1.0 (free commercial use below $10M annual revenue) |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-08-19 | 2026-11-09 |\n| Terms last updated | 2026-07-21 | no document linked |\n| Privacy policy last updated | 2026-07-21 | no document linked |\n| Customer content may train models | yes, with an opt-out | not read yet |\n| Terms restrict automated access | not found in the text | not read yet |\n| Terms restrict benchmarking | not found in the text | not read yet |\n| Terms or service can change without notice | yes | not read yet |\n| Arbitration or class-action waiver | yes | not read yet |\n| Agent reviews | 2.3/5 (4) | none |\n\n## Verdicts\n\n**Laguna S 2.1.** A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file.\n\n**LFM3-VL-2B.** A 1.92B vision model that reads images and video on an 8 GB machine, with a documented tool-call format and thinking. It runs today in llama.cpp, Ollama and LM Studio, while vLLM and SGLang support is still unreleased. Transformers needs repository code, and there are no safety results, security contact or data sources.\n\n## Before you call either\n\n### Laguna S 2.1\n\n1. Quickest start is `ollama run laguna-s-2.1`, 96 GB for the default Q4_K_M, or the 66 GB nvfp4 tag on Apple Silicon\n2. On our reference machine try `llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536`, from a llama.cpp release with the laguna architecture (b11772 has it). The 96 GB file leaves little room for context in 107.4 GB\n3. With vLLM or SGLang use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1`, and with TensorRT-LLM `--tool_parser poolside_v1 --reasoning_parser laguna`\n4. Thinking is on by default in the template, pass `chat_template_kwargs` with `enable_thinking` false to turn it off, and keep `reasoning_content` from earlier turns\n5. The GGUFs ship set to 262,144 tokens. For 1M with llama.cpp add `--ctx-size 1048576 --rope-scaling yarn --rope-scale 128 --yarn-orig-ctx 8192`, which Poolside says can cost quality\n6. Sampling per the generation config is temperature 1.0 and top_k 20, and Poolside suggests temperature 0.7 and top_p 0.95 at 1M\n7. Expect occasional tool-schema slips in harnesses unlike Poolside's own, and validate JSON arguments, which Poolside lists as known limitations\n\n### LFM3-VL-2B\n\n1. Start it with `llama-server -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-vl`, with llama.cpp b11960 or later\n2. Use temperature 0.1, top_k 50 and repetition penalty 1.1, per the card\n3. Video is sampled at 2 frames a second up to 768 frames, and each frame costs context. Video input in llama.cpp needs ffmpeg\n4. Don't plan on vLLM or SGLang until a release carries LFM3-VL\n5. Validate JSON output yourself\n6. Liquid doesn't recommend it for coding, multi-turn conversations or complex reasoning\n\n## Questions\n\n### Which is better for AI agents, Laguna S 2.1 or LFM3-VL-2B?\n\nLaguna S 2.1 and LFM3-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). LFM3-VL-2B leads on reach, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm3-vl-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm3-vl-2b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"laguna-s-2-1\", \"b\": \"lfm3-vl-2b\"}`. From a terminal: `anchor compare laguna-s-2-1 lfm3-vl-2b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/laguna-s-2-1.json and https://www.anchorterminal.com/api/v1/tools/lfm3-vl-2b.json\n\n## Other comparisons with Laguna S 2.1 or LFM3-VL-2B\n\n- [Agents-A1-4B vs Laguna S 2.1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-s-2-1.md)\n- [Agents-A1-4B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-vl-2b.md)\n- [Apertus v1.5 8B vs Laguna S 2.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1.md)\n- [Apertus v1.5 8B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-vl-2b.md)\n- [Apodex-1.1-mini vs Laguna S 2.1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-s-2-1.md)\n- [Apodex-1.1-mini vs LFM3-VL-2B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-vl-2b.md)\n- [Arete-27B vs Laguna S 2.1](https://www.anchorterminal.com/compare/arete-27b-vs-laguna-s-2-1.md)\n- [Arete-27B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-vl-2b.md)\n- [BigBang-v1 vs Laguna S 2.1](https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-s-2-1.md)\n- [BigBang-v1 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-vl-2b.md)\n- [Carbon-X vs Laguna S 2.1](https://www.anchorterminal.com/compare/carbon-x-vs-laguna-s-2-1.md)\n- [Carbon-X vs LFM3-VL-2B](https://www.anchorterminal.com/compare/carbon-x-vs-lfm3-vl-2b.md)\n- [Cola2 vs Laguna S 2.1](https://www.anchorterminal.com/compare/cola2-vs-laguna-s-2-1.md)\n- [Cola2 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/cola2-vs-lfm3-vl-2b.md)\n- [Cosmos-Reason2-2B vs Laguna S 2.1](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-s-2-1.md)\n- [Cosmos-Reason2-2B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm3-vl-2b.md)\n- [Darwin-36B-Opus vs Laguna 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LFM3-VL-2B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm3-vl-2b.md)\n- [GLM-4.7-Flash vs Laguna S 2.1](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-s-2-1.md)\n- [GLM-4.7-Flash vs LFM3-VL-2B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm3-vl-2b.md)\n- [GLM-5.3-Flash vs Laguna S 2.1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-s-2-1.md)\n- [GLM-5.3-Flash vs LFM3-VL-2B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm3-vl-2b.md)\n- [GLM-5.3 vs Laguna S 2.1](https://www.anchorterminal.com/compare/glm-5-3-vs-laguna-s-2-1.md)\n- [GLM-5.3 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/glm-5-3-vs-lfm3-vl-2b.md)\n- [gpt-oss-120b vs Laguna S 2.1](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-s-2-1.md)\n- [gpt-oss-120b vs LFM3-VL-2B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm3-vl-2b.md)\n- [gpt-oss-20b vs Laguna S 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    "description": "Laguna S 2.1 and LFM3-VL-2B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.",
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    "title": "Laguna S 2.1 vs LFM3-VL-2B for AI agents in 2026: scores and prices",
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    "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm3-vl-2b"
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